Decision Support Notebooks, Education

intro to pyemu

intro_to_pyemu Intro to pyEMU¶ This notebook provides a quick run through some of the capabilities of pyemu for working with PEST(++). This run through is

Introduction to Regression

  Introduction to Regression¶ This tutorial proves an overview of linear regression. It illustrates fitting a polynomial to noisy data, including the role of SSE

Non-Linear Uncertainty Analysis

In contrast to linear uncertainty analysis, non-linear methods do not suffer from the limitation of assuming a linear relationship between model predictions and model parameters.

Covariance Matrices: The PPCOV Suite

A variance-covariance matrix, often referred to as a covariance matrix, is a square matrix that provides covariances between pairs of elements of a random vector.

Data Worth Analysis

The present tutorial addresses the ability (or otherwise) of yet-ungathered data to reduce the uncertainties of decision-critical predictions using linear analysis utilities from the PEST

Linear Uncertainty Analysis

Linear uncertainty analysis is also known as “first order second moment” (or “FOSM”) analysis. It provides approximate mathematical characterisation of prior predictive probability distributions, and

Calibration – A Simple Model

This is the first in a series of tutorials which demonstrate workflows for parameter estimation and uncertainty analysis with the PEST/PEST++ suites. These are not the only

SEGLISTS: Interpolation along linear features

This tutorial demonstrates several options for spatial parameterization of linear and polylinear features. In a groundwater model, these may represent entities such as streams, rivers,

PLPROC: a simple pilot point example

PLPROC allows a modeller to create and manipulate parameters that inform hydraulic properties that are represented in a numerical model. In doing this, PLPROC supports